niccolosalvini/notes_rsample

a brief overview of the rsample package

rsample

Niccolò Salvini 25/4/2020

Gentle Introduction

---

rsample contains a set of functions that can create different types of resamples and corresponding classes for their analysis. The goal is to have a modular set of methods that can be used across different R packages for:

• Traditional resampling techniques for estimating the sampling Distribution of a statistic and
• Estimating model performance using a holdout set ( tain test splitting )

In addition it can be used to in combination with recipes and parnsip to develop easy and reproducible model pipelines. it supports train test splitting for a number of different data types: - Survival analisys - Time series Anlisys

It can also be used in: - Tuning models in nested resampling - Gridsearch and randomized search in Keras - Boostrap Interval Estimation

The scope of rsample is to provide the basic building blocks for creating and analyzing resamples of a data set but does not include code for modeling or calculating statistics. The “Working with Resample Sets” vignette gives demonstrations of how rsample tools can be used.

Terminology Alert… (Josh Starmer called upon)

We define a resample as the result of a two-way split of a data set. For example, when bootstrapping, one part of the resample is a sample with replacement of the original data. The other part of the split contains the instances that were not contained in the bootstrap sample. Cross-validation is another type of resampling.

some quick YT reference to refreshboostrapping technique by the statquest. It automatically jumps to minute 8:48, don’t worry.

Application

Everybody knows the mtcars dataset, if you don’t, don’t mind I will introduce you.

# by the skimr package
skimr::skim(mtcars)

Name	mtcars
Number of rows	32
Number of columns	11
_______________________
Column type frequency:
numeric	11
________________________
Group variables	None

Data summary

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
mpg	0	1	20.09	6.03	10.40	15.43	19.20	22.80	33.90	▃▇▅▁▂
cyl	0	1	6.19	1.79	4.00	4.00	6.00	8.00	8.00	▆▁▃▁▇
disp	0	1	230.72	123.94	71.10	120.83	196.30	326.00	472.00	▇▃▃▃▂
hp	0	1	146.69	68.56	52.00	96.50	123.00	180.00	335.00	▇▇▆▃▁
drat	0	1	3.60	0.53	2.76	3.08	3.70	3.92	4.93	▇▃▇▅▁
wt	0	1	3.22	0.98	1.51	2.58	3.33	3.61	5.42	▃▃▇▁▂
qsec	0	1	17.85	1.79	14.50	16.89	17.71	18.90	22.90	▃▇▇▂▁
vs	0	1	0.44	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
am	0	1	0.41	0.50	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▆
gear	0	1	3.69	0.74	3.00	3.00	4.00	4.00	5.00	▇▁▆▁▂
carb	0	1	2.81	1.62	1.00	2.00	2.00	4.00	8.00	▇▂▅▁▁

# using mtcars
set.seed(123)
bt_resamples = bootstraps(mtcars, times = 3)
bt_resamples
#> # Bootstrap sampling 
#> # A tibble: 3 x 2
#>   splits          id        
#>   <list>          <chr>     
#> 1 <split [32/11]> Bootstrap1
#> 2 <split [32/9]>  Bootstrap2
#> 3 <split [32/10]> Bootstrap3

Individual Resamples are rsplit Objects

The resamples are stored in the splits column in an object that has class : rsplit.

In this package we use the following terminology for the two partitions that comprise a resample:

• The analysis data are those that we selected in the resample. For a bootstrap, this is the sample with replacement. For 10-fold cross-validation, this is the 90% of the data. These data are often used to fit a model or calculate a statistic in traditional bootstrapping.(aka the training set even if authors do not reaaly want you to say that)

• The assessment data are usually the section of the original data not covered by the analysis set. Again, in 10-fold CV, this is the 10% held out. These data are often used to evaluate the performance of a model that was fit to the analysis data. aka the testing set even if authors do not reaaly want you to say that)

ed. I am using a Tidyverse approach for the selection of elements in the first_resample, in particular the pull() selection to extract the column split, then the pluck() to pick the first element of the list of the list. (bt_resamples$splits[[1]])

first_resample = 
  bt_resamples %>%
  pull(splits) %>% # pull selection 
  pluck(1)
  
first_resample
#> <Training/Validation/Total>
#> <32/11/32>

This indicates that there were 32 data points in the analysis set, 14 instances were in the assessment set, and that the original data contained 32 data points. These results can also be determined using the dim function on an rsplit object.

How to really call data

as.data.frame(first_resample) %>% 
  head()
#>                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Maserati Bora      15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Cadillac Fleetwood 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Honda Civic        30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Merc 450SLC        15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> Datsun 710         22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Merc 280           19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4

alternatively, way better than this

analysis(first_resample) %>% dim()
#> [1] 32 11
assessment(first_resample) %>% dim()
#> [1] 11 11